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Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

A taxonomy-aware deep learning framework for classifying marine species from underwater imagery.

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Taxonomy-aware deep learning for hierarchical marine species classification in underwater imagery

By Dan Zimmerman, Dimitris A. Pados, George SklivanitisarXiv
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The paper presents a taxonomy-aware deep learning framework that aligns with the hierarchical structure of biological classification. The system combines multiple techniques, including taxonomy-weighted loss and minimum-risk Bayesian inference, to improve classification accuracy.

Evaluated on the FathomNet 2025 dataset, the system achieves competitive results in marine species classification.

Abstract

The paper presents a taxonomy-aware deep learning framework that aligns with the hierarchical structure of biological classification. The system combines multiple techniques, including taxonomy-weighted loss and minimum-risk Bayesian inference, to improve classification accuracy. Evaluated on the FathomNet 2025 dataset, the system achieves competitive results in marine species classification.

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taxonomy-aware deep learningmarine species classificationunderwater imagerybiological classificationOntology & TaxonomyAI AgentsLarge Language ModelsSemantic Interoperability
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